






"""simple docstring"""





import tempfile

import torch

from diffusers import (
    DEISMultistepScheduler,
    DPMSolverMultistepScheduler,
    DPMSolverSinglestepScheduler,
    UniPCMultistepScheduler,
)

from .test_schedulers import SchedulerCommonTest



class       __A		(   SCREAMING_SNAKE_CASE_			):
					UpperCAmelCase__								=				(DEISMultistepScheduler,)
					UpperCAmelCase__								=				(("num_inference_steps", 2_5),)

					def   lowerCamelCase__   (			self  :      Any     ,		**__snake_case  :      Tuple					)			->							str:
											__magic_name__:	Optional[int]							=       {
											    """num_train_timesteps""": 1_0_0_0,
											    """beta_start""": 0.0001,
											    """beta_end""": 0.02,
											    """beta_schedule""": """linear""",
											    """solver_order""": 2,
											}

											config.update(**__snake_case					)
											return config

					def   lowerCamelCase__   (			self  :      Optional[Any]     ,		__snake_case  :      Optional[Any]=0     ,		**__snake_case  :      Any					)			->							List[Any]:
											__magic_name__:	List[str]							=       dict(self.forward_default_kwargs					)
											__magic_name__:	Optional[int]							=       kwargs.pop("""num_inference_steps"""     ,		__snake_case					)
											__magic_name__:	Optional[Any]							=       self.dummy_sample
											__magic_name__:	int							=       0.1 * sample
											__magic_name__:	Union[str, Any]							=       [residual + 0.2, residual + 0.15, residual + 0.10]

											for scheduler_class in self.scheduler_classes:
																	__magic_name__:	Optional[Any]							=       self.get_scheduler_config(**__snake_case					)
																	__magic_name__:	Optional[Any]							=       scheduler_class(**__snake_case					)
																	scheduler.set_timesteps(__snake_case					)
																	# copy over dummy past residuals
																	__magic_name__:	List[str]							=       dummy_past_residuals[: scheduler.config.solver_order]

																	with tempfile.TemporaryDirectory() as tmpdirname:
																							scheduler.save_config(__snake_case					)
																							__magic_name__:	str							=       scheduler_class.from_pretrained(__snake_case					)
																							new_scheduler.set_timesteps(__snake_case					)
																							# copy over dummy past residuals
																							__magic_name__:	Optional[Any]							=       dummy_past_residuals[: new_scheduler.config.solver_order]

																	__magic_name__,			__magic_name__:	Union[str, Any]							=       sample, sample
																	for t in range(__snake_case     ,		time_step + scheduler.config.solver_order + 1					):
																							__magic_name__:	Tuple							=       scheduler.step(__snake_case     ,		__snake_case     ,		__snake_case     ,		**__snake_case					).prev_sample
																							__magic_name__:	Tuple							=       new_scheduler.step(__snake_case     ,		__snake_case     ,		__snake_case     ,		**__snake_case					).prev_sample

																							assert torch.sum(torch.abs(output - new_output					)					) < 1E-5, "Scheduler outputs are not identical"

					def   lowerCamelCase__   (			self  :      Dict					)			->							Any:
											pass

					def   lowerCamelCase__   (			self  :      str     ,		__snake_case  :      Optional[int]=0     ,		**__snake_case  :      Tuple					)			->							List[Any]:
											__magic_name__:	str							=       dict(self.forward_default_kwargs					)
											__magic_name__:	List[Any]							=       kwargs.pop("""num_inference_steps"""     ,		__snake_case					)
											__magic_name__:	Any							=       self.dummy_sample
											__magic_name__:	str							=       0.1 * sample
											__magic_name__:	List[str]							=       [residual + 0.2, residual + 0.15, residual + 0.10]

											for scheduler_class in self.scheduler_classes:
																	__magic_name__:	Dict							=       self.get_scheduler_config()
																	__magic_name__:	Optional[int]							=       scheduler_class(**__snake_case					)
																	scheduler.set_timesteps(__snake_case					)

																	# copy over dummy past residuals (must be after setting timesteps)
																	__magic_name__:	Union[str, Any]							=       dummy_past_residuals[: scheduler.config.solver_order]

																	with tempfile.TemporaryDirectory() as tmpdirname:
																							scheduler.save_config(__snake_case					)
																							__magic_name__:	List[str]							=       scheduler_class.from_pretrained(__snake_case					)
																							# copy over dummy past residuals
																							new_scheduler.set_timesteps(__snake_case					)

																							# copy over dummy past residual (must be after setting timesteps)
																							__magic_name__:	int							=       dummy_past_residuals[: new_scheduler.config.solver_order]

																	__magic_name__:	List[str]							=       scheduler.step(__snake_case     ,		__snake_case     ,		__snake_case     ,		**__snake_case					).prev_sample
																	__magic_name__:	int							=       new_scheduler.step(__snake_case     ,		__snake_case     ,		__snake_case     ,		**__snake_case					).prev_sample

																	assert torch.sum(torch.abs(output - new_output					)					) < 1E-5, "Scheduler outputs are not identical"

					def   lowerCamelCase__   (			self  :      Optional[Any]     ,		__snake_case  :      str=None     ,		**__snake_case  :      Dict					)			->							Union[str, Any]:
											if scheduler is None:
																	__magic_name__:	List[Any]							=       self.scheduler_classes[0]
																	__magic_name__:	str							=       self.get_scheduler_config(**__snake_case					)
																	__magic_name__:	Optional[Any]							=       scheduler_class(**__snake_case					)

											__magic_name__:	List[str]							=       self.scheduler_classes[0]
											__magic_name__:	Any							=       self.get_scheduler_config(**__snake_case					)
											__magic_name__:	Union[str, Any]							=       scheduler_class(**__snake_case					)

											__magic_name__:	Optional[Any]							=       1_0
											__magic_name__:	Dict							=       self.dummy_model()
											__magic_name__:	int							=       self.dummy_sample_deter
											scheduler.set_timesteps(__snake_case					)

											for i, t in enumerate(scheduler.timesteps					):
																	__magic_name__:	Optional[int]							=       model(__snake_case     ,		__snake_case					)
																	__magic_name__:	str							=       scheduler.step(__snake_case     ,		__snake_case     ,		__snake_case					).prev_sample

											return sample

					def   lowerCamelCase__   (			self  :      str					)			->							Dict:
											__magic_name__:	List[str]							=       dict(self.forward_default_kwargs					)

											__magic_name__:	Optional[int]							=       kwargs.pop("""num_inference_steps"""     ,		__snake_case					)

											for scheduler_class in self.scheduler_classes:
																	__magic_name__:	str							=       self.get_scheduler_config()
																	__magic_name__:	Optional[Any]							=       scheduler_class(**__snake_case					)

																	__magic_name__:	Dict							=       self.dummy_sample
																	__magic_name__:	int							=       0.1 * sample

																	if num_inference_steps is not None and hasattr(__snake_case     ,		"""set_timesteps"""					):
																							scheduler.set_timesteps(__snake_case					)
																	elif num_inference_steps is not None and not hasattr(__snake_case     ,		"""set_timesteps"""					):
																							__magic_name__:	List[str]							=       num_inference_steps

																	# copy over dummy past residuals (must be done after set_timesteps)
																	__magic_name__:	int							=       [residual + 0.2, residual + 0.15, residual + 0.10]
																	__magic_name__:	Any							=       dummy_past_residuals[: scheduler.config.solver_order]

																	__magic_name__:	str							=       scheduler.timesteps[5]
																	__magic_name__:	Dict							=       scheduler.timesteps[6]

																	__magic_name__:	List[str]							=       scheduler.step(__snake_case     ,		__snake_case     ,		__snake_case     ,		**__snake_case					).prev_sample
																	__magic_name__:	Tuple							=       scheduler.step(__snake_case     ,		__snake_case     ,		__snake_case     ,		**__snake_case					).prev_sample

																	self.assertEqual(output_a.shape     ,		sample.shape					)
																	self.assertEqual(output_a.shape     ,		output_a.shape					)

					def   lowerCamelCase__   (			self  :      List[Any]					)			->							Dict:
											# make sure that iterating over schedulers with same config names gives same results
											# for defaults
											__magic_name__:	int							=       DEISMultistepScheduler(**self.get_scheduler_config()					)
											__magic_name__:	List[Any]							=       self.full_loop(scheduler=__snake_case					)
											__magic_name__:	Optional[int]							=       torch.mean(torch.abs(__snake_case					)					)

											assert abs(result_mean.item() - 0.23916					) < 1E-3

											__magic_name__:	Dict							=       DPMSolverSinglestepScheduler.from_config(scheduler.config					)
											__magic_name__:	Any							=       DPMSolverMultistepScheduler.from_config(scheduler.config					)
											__magic_name__:	Optional[int]							=       UniPCMultistepScheduler.from_config(scheduler.config					)
											__magic_name__:	int							=       DEISMultistepScheduler.from_config(scheduler.config					)

											__magic_name__:	int							=       self.full_loop(scheduler=__snake_case					)
											__magic_name__:	List[Any]							=       torch.mean(torch.abs(__snake_case					)					)

											assert abs(result_mean.item() - 0.23916					) < 1E-3

					def   lowerCamelCase__   (			self  :      int					)			->							str:
											for timesteps in [2_5, 5_0, 1_0_0, 9_9_9, 1_0_0_0]:
																	self.check_over_configs(num_train_timesteps=__snake_case					)

					def   lowerCamelCase__   (			self  :      Union[str, Any]					)			->							List[Any]:
											self.check_over_configs(thresholding=__snake_case					)
											for order in [1, 2, 3]:
																	for solver_type in ["logrho"]:
																							for threshold in [0.5, 1.0, 2.0]:
																													for prediction_type in ["epsilon", "sample"]:
																																			self.check_over_configs(
																																			    thresholding=__snake_case     ,		prediction_type=__snake_case     ,		sample_max_value=__snake_case     ,		algorithm_type="""deis"""     ,		solver_order=__snake_case     ,		solver_type=__snake_case     ,		)

					def   lowerCamelCase__   (			self  :      int					)			->							Optional[int]:
											for prediction_type in ["epsilon", "v_prediction"]:
																	self.check_over_configs(prediction_type=__snake_case					)

					def   lowerCamelCase__   (			self  :      Optional[Any]					)			->							List[Any]:
											for algorithm_type in ["deis"]:
																	for solver_type in ["logrho"]:
																							for order in [1, 2, 3]:
																													for prediction_type in ["epsilon", "sample"]:
																																			self.check_over_configs(
																																			    solver_order=__snake_case     ,		solver_type=__snake_case     ,		prediction_type=__snake_case     ,		algorithm_type=__snake_case     ,		)
																																			__magic_name__:	Dict							=       self.full_loop(
																																			    solver_order=__snake_case     ,		solver_type=__snake_case     ,		prediction_type=__snake_case     ,		algorithm_type=__snake_case     ,		)
																																			assert not torch.isnan(__snake_case					).any(), "Samples have nan numbers"

					def   lowerCamelCase__   (			self  :      Dict					)			->							str:
											self.check_over_configs(lower_order_final=__snake_case					)
											self.check_over_configs(lower_order_final=__snake_case					)

					def   lowerCamelCase__   (			self  :      int					)			->							Optional[Any]:
											for num_inference_steps in [1, 2, 3, 5, 1_0, 5_0, 1_0_0, 9_9_9, 1_0_0_0]:
																	self.check_over_forward(num_inference_steps=__snake_case     ,		time_step=0					)

					def   lowerCamelCase__   (			self  :      Union[str, Any]					)			->							List[str]:
											__magic_name__:	List[Any]							=       self.full_loop()
											__magic_name__:	Tuple							=       torch.mean(torch.abs(__snake_case					)					)

											assert abs(result_mean.item() - 0.23916					) < 1E-3

					def   lowerCamelCase__   (			self  :      Optional[int]					)			->							Optional[Any]:
											__magic_name__:	Dict							=       self.full_loop(prediction_type="""v_prediction"""					)
											__magic_name__:	Dict							=       torch.mean(torch.abs(__snake_case					)					)

											assert abs(result_mean.item() - 0.091					) < 1E-3






					def   lowerCamelCase__   (			self  :      Union[str, Any]					)			->							List[str]:
											__magic_name__:	int							=       self.scheduler_classes[0]
											__magic_name__:	Optional[Any]							=       self.get_scheduler_config(thresholding=__snake_case     ,		dynamic_thresholding_ratio=0					)
											__magic_name__:	Union[str, Any]							=       scheduler_class(**__snake_case					)

											__magic_name__:	List[Any]							=       1_0
											__magic_name__:	Optional[int]							=       self.dummy_model()
											__magic_name__:	Union[str, Any]							=       self.dummy_sample_deter.half()
											scheduler.set_timesteps(__snake_case					)

											for i, t in enumerate(scheduler.timesteps					):
																	__magic_name__:	str							=       model(__snake_case     ,		__snake_case					)
																	__magic_name__:	List[str]							=       scheduler.step(__snake_case     ,		__snake_case     ,		__snake_case					).prev_sample

											assert sample.dtype == torch.floataa




'''simple docstring'''




def __lowerCAmelCase					(				)      ->       Optional[Any]:
    __lowerCamelCase            =    0
    for i in range(1							,    10_01							):
        total += i**i
    return str(UpperCamelCase__							)[-10:]


if __name__ == "__main__":
  print(solution())
